{
  "abstract": "Magnetic resonance imaging (MRI) and peripheral-blood transcriptomics capture complementary aspects of Alzheimer’s disease (AD), but whether MRI can condition a meaningful molecular representation remains unclear. We developed and independently evaluated an explainable imaging-genetics conditional variational autoencoder (IG-CVAE) in Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed (ANM) cohorts using fixed 80% train-validation and 20% independent test sets with five-fold cross-validation. IG-CVAE comprises a genetic branch encoding expression into 128-dimensional gene-feature vectors (GFV128), an imaging branch encoding MRI into 128-dimensional MRI-feature vectors (MFV128), and a conditional variational autoencoder that generates GFV128 from MFV128. Paired analyses included 289 ADNI and 98 ANM participants. Test area under the receiver operating characteristic curve (ROC-AUC) values in ADNI/ANM were 0.646/0.851 for the genetic branch, 0.942/0.752 for the imaging branch, and 0.939/0.869 for generated GFV128. Generated-observed GFV128 agreement was moderate (cosine similarity, 0.590/0.589; coefficient of determination (R2), 0.161/0.211). Molecular importance was cohort-specific at the gene level, with no significant pathway-level convergence, whereas MRI perturbation showed cross-cohort convergence in cerebral white matter linked to distinct disease-informative molecular features. Overall, structural MRI retained disease-relevant information sufficient to generate disease-informative molecular representations, supporting IG-CVAE as a participant-paired hypothesis-generating imaging–transcriptomic framework while providing only limited evidence of participant-specific molecular correspondence without implying raw transcriptome reconstruction. The ANM test estimates should be interpreted cautiously given the small paired independent test set (n = 20).",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA",
    "Department of Computer Science, Indiana University Bloomington, Bloomington, IN 47408, USA",
    "Department of Mathematics, Indiana University School of Science, Indianapolis, IN 46202, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaoqing Huang",
      "orcid": "0009-0003-2183-8398"
    },
    {
      "affs": [
        1
      ],
      "name": "Md Hasan Imam Shihab",
      "orcid": "0009-0009-9061-4228"
    },
    {
      "affs": [
        1
      ],
      "name": "Ronakkumar Laxmanbhai Bhagchandani",
      "orcid": "0009-0006-1255-2023"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Yuxin Dong"
    },
    {
      "affs": [
        1
      ],
      "name": "Yijie Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmhx7ddypspbe.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27219741",
  "fulltext_url": null,
  "id": "bmhx7ddypspbe",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/21/9741",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/bmhx7ddypspbe.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:54Z",
      "native_id": "10.3390/ijms27219741",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/21/9741"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:1422-0067",
      "subfield": "general"
    }
  ],
  "title": "An Explainable MRI-Conditioned Generative Framework for Gene-Feature Representations in Alzheimer’s Disease",
  "title_text": "An Explainable MRI-Conditioned Generative Framework for Gene-Feature Representations in Alzheimer’s Disease",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:52Z",
  "url": "https://research.draconitic.com/paper/bmhx7ddypspbe",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}